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2022 International Journal of Computer Vision and Image Processing  
By using this hybrid moth flame optimization, the feature set is reduced to 40%.  ...  In this paper, an improved technique for feature selection by Moth Flame Optimization with Opposition Based Learning (OBL) and Simulated Annealing (OB-MFOSA) is proposed.  ...  Moth Flame Optimization, a swarm intelligence optimization algorithm, is a searching method for selecting an optimal feature set.  ... 
doi:10.4018/ijcvip.296585 fatcat:cbymyd5rmfdndpxwnfv6xtugfe

Ameliorated moth-flame algorithm and its application for modeling of silicon content in liquid iron of blast furnace based fast learning network

Xiaodong Zhao, Yiming Fang, Le Liu, Miao Xu, Pan Zhang
2020 Applied Soft Computing  
Moth-flame optimization algorithm The Moth-Flame Optimization (MFO) algorithm is a new stochastic population-based algorithm.  ...  Abstract Moth-Flame Optimization (MFO) algorithm is a widely used nature-inspired optimization algorithm.  ...  including Moth-Flame Optimization (MFO) algorithm, Lévy-flight Moth-Flame Optimization (LMFO), Grey Wolf Optimization (GWO) algorithm, Sine Cosine Algorithm (SCA), Gravitational Search Algorithm (GSA)  ... 
doi:10.1016/j.asoc.2020.106418 fatcat:mgve4gvw6rfcrkpc5h7pifw3fi

Short-Term Operational Scheduling of Unit Commitment Using Binary Alternative Moth-Flame Optimization

Soraphon Kigsirisin, Hajime Miyauchi
2021 IEEE Access  
Developed from moth-flame optimization (MFO), the proposed method is called binary alternative MFO (BAMFO).  ...  This study proposes a novel binary optimization method for solving unit commitment (UC) problems.  ...  With these features, for example, compared with other metaheuristic methods, MFO yielded better results in economic dispatch problems [29] , [30] , software fault prediction datasets [31] , and energy  ... 
doi:10.1109/access.2021.3051175 fatcat:weua6g5wazggzklpvuuk3ww3b4

Optimal Allocation of IaaS Cloud Resources through Enhanced Moth Flame Optimization (EMFO) Algorithm

Srinivasan Thiruvenkadam, Hyung-Jin Kim, In-Ho Ra
2022 Electronics  
This paper proposes the Enhanced Moth Flame Optimization (EMFO) algorithm to provide a unique strategy for assigning virtual machines to suit customer requirements.  ...  Given the opposing aims of increasing customer demand fulfillment while decreasing costs and optimizing asset efficiency, efficient VM allocation is generally considered as one of the most difficult tasks  ...  Enhanced Moth Flame Optimization (EMFO) is presented in this work to address the issue of optimal resource allocation in IaaS administration.  ... 
doi:10.3390/electronics11071095 fatcat:oze3gpdubfa7zpkfs3cs6ojbbe

Predicting Green Consumption Behaviors of Students using Efficient Firefly Grey Wolf-assisted K-Nearest Neighbor Classifiers

Hua Tang, Yueting Xu, Aiju Lin, Ali Asghar Heidari, Mingjing Wang, Huiling Chen, Yungang Luo, Chengye Li
2020 IEEE Access  
Hopefully, the established adaptive OBLFA_GWO-KNN model can be considered as a useful tool for predicting students' behavior of green consumption.  ...  Then, the enhanced KNN model is used to identify the importance and interrelationships of features in samples and construct an effective and stable predictive model for decision support.  ...  Also, the binary OBLFA_GWO algorithm is proposed here as a feature selection tool to select the critical feature subset.  ... 
doi:10.1109/access.2020.2973763 fatcat:ck43o7n2w5bdvfgnmcl7nynneu

Software Project Management Using Machine Learning Technique—A Review

Mohammed Najah Mahdi, Mohd Hazli Mohamed Zabil, Abdul Rahim Ahmad, Roslan Ismail, Yunus Yusoff, Lim Kok Cheng, Muhammad Sufyian Bin Mohd Azmi, Hayder Natiq, Hushalini Happala Naidu
2021 Applied Sciences  
efficiently reduce the project failure probabilities, and increasing the output ratio for growth, and it also facilitates analysis on software fault prediction based on accuracy.  ...  Without a realistic and logical plan, it isn't easy to handle project management efficiently.  ...  Ref Type of ML Datasets Model Achieve Prediction Advantages Limitation [42] KNN Several dataset EBMFO Accuracy 89% Enhanced Binary Moth Flame Optimization (EBMFO) with Adaptive synthetic  ... 
doi:10.3390/app11115183 fatcat:e7cfhrwyunfeld7cpngu4uifl4

A Review of the Modification Strategies of the Nature Inspired Algorithms for Feature Selection Problem

Ruba Abu Abu Khurma, Ibrahim Aljarah, Ahmad Sharieh, Mohamed Abd Abd Elaziz, Robertas Damaševičius, Tomas Krilavičius
2022 Mathematics  
This survey is an effort to provide a research repository and a useful reference for researchers to guide them when planning to develop new Nature-inspired Algorithms tailored to solve Feature Selection  ...  We identified and performed a thorough literature review in three main streams of research lines: Feature selection problem, optimization algorithms, particularly, meta-heuristic algorithms, and modifications  ...  This adaptive update strategy enhanced the performance of the optimizer. In [148] , a time varying flame strategy was proposed to enhance the MFO algorithm.  ... 
doi:10.3390/math10030464 fatcat:sjg667gilzfktokxxjwdg52jbm

Selecting critical features for data classification based on machine learning methods

Rung-Ching Chen, Christine Dewi, Su-Wen Huang, Rezzy Eko Caraka
2020 Journal of Big Data  
Random Forest has emerged as a quite useful algorithm that can handle the feature selection issue even with a higher number of variables.  ...  The reduction of the original feature that set to a smaller one is preserving the relevant information while discarding the redundant one, and it is referred to feature selection (FS) [6, 7] .To solve  ...  Acknowledgements The authors would like to thank all the colleagues from Chaoyang University of Technology (Taiwan), Satya Wacana Christian University (Indonesia), Taichung Veterans General Hospital (Taiwan  ... 
doi:10.1186/s40537-020-00327-4 fatcat:volleohr3zflpiqyrs6es5nuv4

Crow Search Algorithm: Theory, Recent Advances, and Applications

Abdelazim G. Hussien, Mohamed Amin, Mingjing Wang, Guoxi Liang, Ahmed Alsanad, Abdu Gumaei, Huiling Chen
2020 IEEE Access  
A comparison among many MA has been made including Grey Wolf Optimization, Particle Swarm Optimization, Sine Cosine Algorithm, Bat Algorithm, Firefly Algorithm, Moth-Flame Optimization, Whale Optimization  ...  COMPUTER SCIENCE In this subsection, all CSA applications related to computer science have been discussed. 1) Feature selection Feature selection (FS) can be defined as the process of selecting the most  ...  His current research interests include machine learning and data mining, as well as their applications to medical diagnosis and bankruptcy prediction.  ... 
doi:10.1109/access.2020.3024108 fatcat:w2yitdsuzvgxpkwhsgodall2qm

IBDA: Improved binary dragonfly algorithm with evolutionary population dynamics and adaptive crossover for feature selection

Jiahui Li, Hui Kang, Geng Sun, Tie Feng, Wenqi Li, Wei Zhang, Bai Ji
2020 IEEE Access  
[48] propose an enhanced binary moth flame optimization (EBMFO) with adaptive synthetic sampling (ADASYN) to predict the most optimal feature combination in software faults.  ...  In [38] , a feature selection algorithm based on the moth-flame optimization (MFO) is proposed. Moreover, Emary et al.  ... 
doi:10.1109/access.2020.3001204 fatcat:prc6yabvjvh4fozt2vhnven5ky


2019 2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)  
The presented research work can identify the air conditioner which is faulty as well as predicts the type of fault at an early stage to do maintenance beforehand. pp. 154-159 A Study of Distribution  ...  generated from the gas contents (H2, CH4, C2H2, C2H4 and C2H6) and the selection of Optimal inputs (VE-BPSO) is extracted with Binary Particle Swarm Optimization (BPSO) in the nearest neighbor classification  ...  The system study is conducted for different loading conditions, and the proposed algorithm is compared with state of the art algorithms, namely, grey wolf optimization, and moth flame optimization.  ... 
doi:10.1109/isap48318.2019.9065935 fatcat:n5yuhvudcfemzhr4tazxeei4fi

Multi-Strategy Ensemble Whale Optimization Algorithm and Its Application to Analog Circuits Intelligent Fault Diagnosis

Xianfeng Yuan, Zhaoming Miao, Ziao Liu, Zichen Yan, Fengyu Zhou
2020 Applied Sciences  
Therefore, the MSWOA is successfully applied as a novel and efficient optimization algorithm.  ...  This paper proposes a multi-strategy ensemble whale optimization algorithm (MSWOA) to alleviate these deficiencies.  ...  Acknowledgments: We would like to thank the editors and the anonymous reviewers for their insightful comments and constructive suggestions.  ... 
doi:10.3390/app10113667 fatcat:4y4erzmfebaxvbzu7dfhhukh3q

Application of Artificial Intelligence in Predicting Earthquakes: State-of-the-Art and Future Challenges

Md. Hasan Al Banna, Kazi Abu Taher, M. Shamim Kaiser, Mufti Mahmud, Md. Sazzadur Rahman, A. S. M. Sanwar Hosen, Gi Hwan Cho
2020 IEEE Access  
ACKNOWLEDGMENT The authors would like to thank Bangladesh University of Professionals for supporting this research.  ...  [99] proposed a functional link ANN optimized by the moth flame optimization (MFO) algorithm to predict the magnitude of an earthquake.  ...  Based on that, a rank was given to the samples. Then seven best features were selected and used as input to an ANN model.  ... 
doi:10.1109/access.2020.3029859 fatcat:m53zn4ulq5c2neezhqa53qirca

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

Faiza Gul, Imran Mir, Laith Abualigah, Putra Sumari, Agostino Forestiero
2021 Electronics  
Moreover, optimization techniques suitable for implementing ground, aerial, and underwater vehicles are also a part of this review.  ...  In this paper, a review on the three most important communication techniques (ground, aerial, and underwater vehicles) has been presented that throws light on trajectory planning, its optimization, and  ...  [220] proposed a Moth Flame Optimization Algorithm for fault resilient issues in autonomous underwater vehicles (AUVs).  ... 
doi:10.3390/electronics10182250 fatcat:xxbfslyes5ctpfktnprezb6p6m

Applications and Challenges of Artificial Intelligence in Space Missions

Paul A. Oche, Gideon A. Ewa, Nwanneka Ibekwe
2021 IEEE Access  
These limitations have necessitated the need to have a concise survey with a wider scope for those interested in the applications and challenges of AI in the space industry, especially those with technical  ...  The first category suffers from the limitation of being old and not covering some crucial and recent developments in the field; such as the contributions of Deep Learning (DL) and bioinspired AI algorithms  ...  Moth Flame Optimization (MFO) is a novel nature-inspired optimization paradigm inspired by the navigation method of moths in nature called transverse orientation [64] . i.  ... 
doi:10.1109/access.2021.3132500 fatcat:2n5el5dcqzgdtc3brca5xwxrfu
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